Computational cytology is a critical, rapid-developing, yet challenging topic in medical
image computing concerned with analyzing digitized cytology images by computer-aided …

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche
terms that are increasingly appearing in scientific presentations as well as in the general …

Ninety years after its invention, the Pap test continues to be the most used method for the
early identification of cervical precancerous lesions. In this test, the cytopathologists look for …

In many computerized methods for cell detection, segmentation, and classification in digital
histopathology that have recently emerged, the task of cell segmentation remains a chief …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …

Recently, deep learning frameworks have been shown to be successful and efficient in
processing digital histology images for various detection and classification tasks. Among …

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and
classical image processing algorithms are most commonly used for this task. Recent …

Background: Deep learning (DL) is a representation learning approach ideally suited for
image analysis challenges in digital pathology (DP). The variety of image analysis tasks in …

In histopathological image assessment, there is a high demand to obtain fast and precise
quantification automatically. Such automation could be beneficial to find clinical assessment …